Prostate Cancer Studies Prostate cancer (PCa) is the most common solid tumor in (humans) men and is a major cause of cancer-related morbidity and mortality, yet its etiology and molecular underpinnings are unresolved (Eeles et al., 2014). Last year, more than 161,360 men were diagnosed with PCa, and about 27,000 were predicted to die of the disease. We have worked for over 25 years to identify genes and markers associated with disease risk, progression and outcomes. Much of our work has been in consortia, or with long time collaborator Dr. Janet Stanford at the Fred Hutchinson Cancer Research Center in Seattle WA. Risk of Aggressive and Lethal Prostate Cancer Biomarkers to identify men who at high risk of metastatic disease are needed for prostate cancer. We have worked with the Stanford group to continue to identify germline variants associated with prostate cancer specific mortality. In a study by FitzGerald et al., (FitzGerald et al., 2018). we analyzed 12,082 PC cases and found variants in IL4, MGMT and AKT1 are associated PC mortality, providing evidence that genetic background plays a role in modulating tumor aggressiveness. Predictive Models We found that 13 of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset (Cheng et al., 2019). These biomarkers plus Gleason Score were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P=5.710-11 ). Thus, our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility (Cheng et al., 2019). By comparison, we reported that a five CpG DNA methylation score could predict metastatic lethal outcomes in men treated with radical prostatectomy for localized prostate cancer (Zhao et al., 2018). Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r>0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset. We found that five CpGs were technically validated and all were retained (P<0.05) in the final model (Zhao et al., 2018). The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P=6.810-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR=4.0, 95%CI=1.8-14.3). The score demonstrated better prediction performance (AUC=0.91; pAUC=0.037) compared to Gleason sum alone (AUC=0.87; pAUC=0.025), and warrants further evaluation as a tool for improving patient outcomes. Consortia We continued our participation in large consortia, publishing multiple high profile papers. For instance, in Matajcic et al., 2018, the consortium combined genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. Twelve independent risk signals for prostate cancer were found (p<4.2810-15), including three risk variants that were newly reported. These 12 variants account for 25% of familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification. Also working in the very large PRACTICAL consortium, the collaborative group applied a Bayesian multivariate variable selection algorithm, JAM, to fine-map 84 prostate cancer susceptibility loci using summary data from a large European ancestry meta-analysis (Dadaev et al., 2019). The consortium observed evidence for multiple independent signals at 12 regions and 99 risk signals overall. Biological annotation of the credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. The refined set of candidate variants substantially increases the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling (Dadaev et al., 2019). Finally, in a large study of over 140,000 men that we were part of, 63 new prostate cancer susceptibility loci were identified (Schumacher et al., 2018) (P<5.010-8), with one locus significantly associated with early-onset disease (less than 55 years). The findings include missense variants rs1800057 (odds ratio (OR)=1.16; P=8.210-9; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR=1.06; P=2.310-9; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk=2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk=5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average.